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Xulu KR, Nweke EE, Augustine TN. Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches. Front Genet 2023; 14:1087432. [PMID: 37662839 PMCID: PMC10469897 DOI: 10.3389/fgene.2023.1087432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 08/08/2023] [Indexed: 09/05/2023] Open
Abstract
The burden of breast cancer continues to increase worldwide as it remains the most diagnosed tumor in females and the second leading cause of cancer-related deaths. Breast cancer is a heterogeneous disease characterized by different subtypes which are driven by aberrations in key genes such as BRCA1 and BRCA2, and hormone receptors. However, even within each subtype, heterogeneity that is driven by underlying evolutionary mechanisms is suggested to underlie poor response to therapy, variance in disease progression, recurrence, and relapse. Intratumoral heterogeneity highlights that the evolvability of tumor cells depends on interactions with cells of the tumor microenvironment. The complexity of the tumor microenvironment is being unraveled by recent advances in screening technologies such as high throughput sequencing; however, there remain challenges that impede the practical use of these approaches, considering the underlying biology of the tumor microenvironment and the impact of selective pressures on the evolvability of tumor cells. In this review, we will highlight the advances made thus far in defining the molecular heterogeneity in breast cancer and the implications thereof in diagnosis, the design and application of targeted therapies for improved clinical outcomes. We describe the different precision-based approaches to diagnosis and treatment and their prospects. We further propose that effective cancer diagnosis and treatment are dependent on unpacking the tumor microenvironment and its role in driving intratumoral heterogeneity. Underwriting such heterogeneity are Darwinian concepts of natural selection that we suggest need to be taken into account to ensure evolutionarily informed therapeutic decisions.
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Affiliation(s)
- Kutlwano Rekgopetswe Xulu
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ekene Emmanuel Nweke
- Department of Surgery, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Tanya Nadine Augustine
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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2
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Zhang Y, Yang X, Zhang Y, Zhang Y, Wang M, Ou JX, Zhu Y, Zeng H, Wu J, Lan C, Zhou HW, Yang W, Zhang Z. Tools for fundamental analysis functions of TCR repertoires: a systematic comparison. Brief Bioinform 2021; 21:1706-1716. [PMID: 31624828 PMCID: PMC7947996 DOI: 10.1093/bib/bbz092] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 07/02/2019] [Accepted: 07/05/2019] [Indexed: 12/30/2022] Open
Abstract
The full set of T cell receptors (TCRs) in an individual is known as his or her TCR repertoire. Defining TCR repertoires under physiological conditions and in response to a disease or vaccine may lead to a better understanding of adaptive immunity and thus has great biological and clinical value. In the past decade, several high-throughput sequencing-based tools have been developed to assign TCRs to germline genes and to extract complementarity-determining region 3 (CDR3) sequences using different algorithms. Although these tools claim to be able to perform the full range of fundamental TCR repertoire analyses, there is no clear consensus of which tool is best suited to particular projects. Here, we present a systematic analysis of 12 available TCR repertoire analysis tools using simulated data, with an emphasis on fundamental analysis functions. Our results shed light on the detailed functions of TCR repertoire analysis tools and may therefore help researchers in the field to choose the right tools for their particular experimental design.
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Affiliation(s)
- Yanfang Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.,Center for Biomedical Informatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China.,Center for Precision Medicine, Shunde Hospital, Southern Medical University, Foshan, Guangdong, 528399, China
| | - Xiujia Yang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.,Center for Biomedical Informatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China
| | - Yanxia Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.,Center for Biomedical Informatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yan Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.,Center for Biomedical Informatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Minhui Wang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Jin Xia Ou
- Microbiome Medicine Center, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Yan Zhu
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.,Center for Biomedical Informatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Huikun Zeng
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.,Center for Biomedical Informatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jiaqi Wu
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.,Center for Biomedical Informatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Chunhong Lan
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.,Center for Biomedical Informatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China.,Center for Precision Medicine, Shunde Hospital, Southern Medical University, Foshan, Guangdong, 528399, China
| | - Hong-Wei Zhou
- Microbiome Medicine Center, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Wei Yang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China.,Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Zhenhai Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.,Center for Biomedical Informatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China.,Center for Precision Medicine, Shunde Hospital, Southern Medical University, Foshan, Guangdong, 528399, China
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4
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Adabor ES, Acquaah-Mensah GK. DOKI: Domain knowledge-driven inference method for reverse-engineering transcriptional regulatory relationships among genes in cancer. Comput Biol Med 2020; 125:104017. [PMID: 33010618 DOI: 10.1016/j.compbiomed.2020.104017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/16/2020] [Accepted: 09/20/2020] [Indexed: 11/18/2022]
Abstract
Efficient reverse-engineering methods are important for identifying transcriptional regulatory relationships among genes in cancer. These methods are becoming increasingly useful in this era where huge volumes of data are generated through the use of high-throughput technologies such as next-generation sequencing technologies and microarrays. However, it is important to improve current methods because of complications involved in modelling complex biological systems. In this paper, we present a novel approach, Domain Knowledge-driven Inference (DOKI), for identification of transcriptional regulatory relationships among genes, given a biological context such as cancer. Combining data normalization, the use of a probability distribution function and Kullback-Leibler Divergence, DOKI incorporates a domain knowledge-driven criterion to make determinations of the existence of regulatory relationships between given transcription factors and given specific gene targets. Characteristics of DOKI enable it to adequately handle complexities inherent in data, and accurately unearth linear and higher-order dependent relationships among genes. DOKI performed equally well with one established high-performing method and better than three other high-performing methods on relatively small data sets. However, it remarkably outperformed these methods on larger data sets to demonstrate its utility. Furthermore, we demonstrate the relevance of such inference algorithms for identifying novel relationships among genes in breast cancer, as some of the consensus results representing novel relationships were confirmed in previously published experimental results. Thus, DOKI will facilitate current efforts to gain etiological insights and help uncover new targeted therapies for various diseases.
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Affiliation(s)
- Emmanuel S Adabor
- School of Technology, Ghana Institute of Management and Public Administration, Achimota, Accra, Ghana.
| | - George K Acquaah-Mensah
- Pharmaceutical Sciences Department, Massachusetts College of Pharmacy and Health Sciences (MCPHS University), 19 Foster Street, Worcester, MA, USA
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Adabor ES, Acquaah-Mensah GK, Mazandu GK. MSclassifier: median-supplement model-based classification tool for automated knowledge discovery. F1000Res 2020; 9:1114. [PMID: 33456763 PMCID: PMC7788522 DOI: 10.12688/f1000research.25501.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/03/2020] [Indexed: 11/20/2022] Open
Abstract
High-throughput technologies have resulted in an exponential growth of publicly available and accessible datasets for biomedical research. Efficient computational models, algorithms and tools are required to exploit the datasets for knowledge discovery to aid medical decisions. Here, we introduce a new tool, MSclassifier, based on median-supplement approaches to machine learning to enable an automated and effective binary classification for optimal decision making. The MSclassifier package estimates medians of features (attributes) to deduce supplementary data, which is subsequently introduced into the training set for balancing and building superior models for classification. To test our approach, it is used to determine HER2 receptor expression status phenotypes in breast cancer and also predict protein subcellular localization (plasma membrane and nucleus). Using independent sample and cross-validation tests, the performance of MSclassifier is evaluated and compared with well established tools that could perform such tasks. In the HER2 receptor expression status phenotype identification tasks, MSclassifier achieved statistically significant higher classification rates than the best performing existing tool (90.30% versus 89.83%, p=8.62e-3). In the subcellular localization prediction tasks, MSclassifier and one other existing tool achieved equally high performances (93.42% versus 93.19%, p=0.06) although they both outperformed tools based on Naive Bayes classifiers. Overall, the application and evaluation of MSclassifier reveal its potential to be applied to varieties of binary classification problems. The MSclassifier package provides an R-portable and user-friendly application to a broad audience, enabling experienced end-users as well as non-programmers to perform an effective classification in biomedical and other fields of study.
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Affiliation(s)
- Emmanuel S. Adabor
- School of Technology, Ghana Institute of Management and Public Administration, Accra, Ghana
| | - George K. Acquaah-Mensah
- Pharmaceutical Sciences Department, Massachusetts College of Pharmacy and Health Sciences, Worcester, MA, USA
| | - Gaston K. Mazandu
- African Institute for Mathematical Sciences and Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
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Tang J, Wang Y, Luo Y, Fu J, Zhang Y, Li Y, Xiao Z, Lou Y, Qiu Y, Zhu F. Computational advances of tumor marker selection and sample classification in cancer proteomics. Comput Struct Biotechnol J 2020; 18:2012-2025. [PMID: 32802273 PMCID: PMC7403885 DOI: 10.1016/j.csbj.2020.07.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.
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Key Words
- ANN, Artificial Neural Network
- ANOVA, Analysis of Variance
- CFS, Correlation-based Feature Selection
- Cancer proteomics
- Computational methods
- DAPC, Discriminant Analysis of Principal Component
- DT, Decision Trees
- EDA, Estimation of Distribution Algorithm
- FC, Fold Change
- GA, Genetic Algorithms
- GR, Gain Ratio
- HC, Hill Climbing
- HCA, Hierarchical Cluster Analysis
- IG, Information Gain
- LDA, Linear Discriminant Analysis
- LIMMA, Linear Models for Microarray Data
- MBF, Markov Blanket Filter
- MWW, Mann–Whitney–Wilcoxon test
- OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis
- PCA, Principal Component Analysis
- PLS-DA, Partial Least Square Discriminant Analysis
- RF, Random Forest
- RF-RFE, Random Forest with Recursive Feature Elimination
- SA, Simulated Annealing
- SAM, Significance Analysis of Microarrays
- SBE, Sequential Backward Elimination
- SFS, and Sequential Forward Selection
- SOM, Self-organizing Map
- SU, Symmetrical Uncertainty
- SVM, Support Vector Machine
- SVM-RFE, Support Vector Machine with Recursive Feature Elimination
- Sample classification
- Tumor marker selection
- sPLSDA, Sparse Partial Least Squares Discriminant Analysis
- t-SNE, Student t Distribution
- χ2, Chi-square
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Affiliation(s)
- Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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